How Generative AI in Day-to-Day Workflows Can Boost Efficiency and Reduce Stress

Over the past few years, the U.S workforce has experienced significant changes. Many employers quickly introduced hybrid work models to adapt to the COVID-19 pandemic and several continue to focus on meeting employee demands for improved work-life balance while also grappling with inflation, hiring freezes, layoffs, and in some industries, labor shortages. Recently, artificial intelligence (AI) has emerged as the latest disruptor to business as usual and may be the key to alleviating many workforce challenges.

Progress in AI seems to be skyrocketing, partially due to the recent popularity of generative AI tools, such as OpenAI’s ChatGPT, a large language model (LLM) that has attracted millions of users with its easy-to-use natural language prompt input format for instantly generating remarkable content, Google’s long-awaited Bard chatbot, and OpenAI’s creatively-named art generation tool, DALL-E, which turned the spotlight on AI ethical concerns.

Generative AI is a compelling, groundbreaking technology that has broad applications in the modern workplace.

The potential of AI systems to enhance productivity and improve work-life balance is a topic of great excitement. While much attention gets placed on generative AI’s ability to produce artistic content, these tools have use cases across various industries. It is estimated that 25 percent of current work tasks in the U.S. and Europe could be automated by AI, according to a recent Goldman Sachs report. Whether it's streamlining mundane tasks or making complex projects more approachable, generative AI provides an array of capabilities and is becoming increasingly relevant as it helps companies automate tasks and streamline workflows, saving time and reducing costs.

For example:

• Call center operators using generative AI saw productivity increases of 14%, with even greater gains for inexperienced workers, according to a recent MIT study. Not only did this technology improve customer sentiments, but it also supported the transfer of tacit knowledge among employees, leading to lower attrition rates.
• Software engineers can code much faster using OpenAI’s GPT-3's predecessor tool, Codex.
• Sales teams can use generative AI to analyze sales data, optimize processes, generate personalized content, and more.

Enterprises looking to integrate generative AI into daily workflows can employ an off-the-shelf generative AI solution, leverage a foundation model (the neural network or “brain” that powers a chatbot) with an API, or if budget allows, customize a foundation model from scratch using unique data, such as customer data, internal company data or a narrow set of data specific to a certain topic or industry in order to fit unique business needs. Using an off-the-shelf tool is usually the least expensive option, making it attractive for organizations that are not yet ready to invest in a customized solution. However, it is important to understand the difference between public and enterprise-level AI platforms.

Public and enterprise-grade AI platforms have their differences, and one of the most significant disparities is the level of security they offer. Enterprise-grade platforms provide advanced security features such as multifactor authentication, encryption and role-based access control. These features provide more protection for sensitive data and help prevent unauthorized access. Additionally, enterprise-grade platforms typically have more stringent copyright and intellectual property protections. They have strict policies in place to prevent the unauthorized use of copyrighted materials or intellectual property. Furthermore, enterprise-level AI platforms often offer granular control over data usage, allowing businesses to adhere to regulations and internal policies.

One example of using an enterprise-grade platform is to utilize Azure OpenAI Service for creating models and data sets to generate predictions and identify trends, and Azure Machine Learning to build predictive models that forecast market trends based on historical data that has been properly vetted and cleaned.

Public AI platforms are perfectly fine if your organization is not concerned about being included in the product (data) set but be aware that even OpenAI says that “for non-API consumer products like ChatGPT and DALL-E, OpenAI may use content such as prompts, responses, uploaded images, and generated images to improve their services.”

As more workers integrate AI into their daily routine, we can expect to see industry-wide transformations that revolutionize productivity. A Goldman Sachs report estimates the boost to global labor productivity could be economically significant, and estimates that AI could eventually increase annual global GDP by 7 percent. 

While organizations reap the benefits of improved productivity, employees can benefit from less work-related stress and improved work-life balance by using generative AI to tackle repetitive, error-prone tasks or to jumpstart more meaningful work. For example, generative AI can help development teams get through a backlog of tasks so they can prioritize delivering better products, marketing teams can gain more time to experiment with personalized marketing campaigns, lawyers can more efficiently conduct research and draft documents and video editors can quickly change the color of objects.

The success of generative AI lies in its ability to learn from data and adapt over time and our ability to work with it, making it more accurate and efficient as it is used. By augmenting human skills instead of replacing them, generative AI is a valuable tool for the modern workplace and can help organizations meet the need to strengthen employee satisfaction, reduce workplace stress, improve work-life balance, retain employees and more.